code: def main(args):
      experiment_with_models = defaultdict(list)
      run_paths = glob(os.path.join(ROOT, "outputs", "*", "*"))
      for run_path in run_paths:
          experiment = ExperimentFolder(run_path)
          if experiment.contains_trained_model:
              experiment_with_models[experiment.model_name].append(
                  experiment
              )
          else:
              if args.d:
                  shutil.rmtree(run_path)
  
      print("")
      for model_name in experiment_with_models.keys():
          colored_print(COLORS.Green, str(model_name))
          for experiment in experiment_with_models[model_name]:
              print(experiment)
              num_epoch, stats = experiment.extract_stats()
              colored_print(COLORS.Red, "Epoch: {}".format(num_epoch))
              for metric_name in stats:
                  sentence = ""
                  for split_name in stats[metric_name].keys():
                      sentence += "{}: {}, ".format(
                          split_name, stats[metric_name][split_name]
                      )
                  metric_sentence = metric_name + "({})".format(
                      sentence[:-2]
                  )
                  colored_print(COLORS.BBlue, metric_sentence)
              print("")
          print("")
  
      if args.pdb:
          import pdb
  
          pdb.set_trace()
  
file_path: torch-points3d/scripts/find_runs.py
imports: import os
  import sys
  import argparse
  from glob import glob
  from collections import defaultdict
  import torch
  import shutil
  from torch_points3d.utils.colors import COLORS
label_desc: Use image node_id 2 for calling a global function with example usage: main(args) without return types